Assessment of a Radiomics-Based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules

基于放射组学的计算机辅助诊断工具对肺结节癌症风险分层的评估

基本信息

批准号：
10644765
负责人：
Roger Yeon-Kyu Kim
金额：
$ 24.96万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2028-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10644765
关键词：
Address Adult Advisory Committees Agreement Award Benign Biometry Biopsy Cancer Center Classification Clinic Clinical Clinical Management Clinical Oncology Clinical Research Clinical Trials Clinical Trials Design Clinical effectiveness Complex Computer-Assisted Diagnosis Cost Effectiveness Analysis Data Development Plans Diagnosis Diagnostic Early Diagnosis Effectiveness Epidemiology Evaluation Eye Face Financial Support Goals Human Image Individual Institution Intervention K-Series Research Career Programs Knowledge Lung Lung nodule Malignant - descriptor Malignant Neoplasms Malignant neoplasm of lung Malignant neoplasm of thorax Mentors Mentorship Methods Modeling Outcome Patient-Focused Outcomes Patients Pennsylvania Performance Physicians Positioning Attribute Postdoctoral Fellow Probability Procedures Process Publishing Pulmonary Challenge Pulmonology Quality-Adjusted Life Years Research Research Personnel Research Training Retrospective cohort Retrospective cohort study Risk Risk Estimate Scientist Testing Thoracic Oncology Time Training Universities Use Effectiveness Vision X-Ray Computed Tomography base cancer care cancer diagnosis cancer risk career career development clinical practice clinical risk clinically significant cohort collaborative environment comparative effectiveness trial cost cost effective cost effectiveness cost-effectiveness evaluation diagnostic accuracy effectiveness evaluation experience improved indexing innovation lung cancer screening multidisciplinary new technology novel participant enrollment primary outcome radiomics randomized, clinical trials research clinical testing risk prediction model risk stratification serial imaging skill acquisition supportive environment tool treatment as usual uptake

项目摘要

PROJECT SUMMARY/ABSTRACT This proposal describes a comprehensive 5-year mentored career development plan with the goal of training the candidate to become a leading independent physician-scientist focused on improving the diagnostic evaluation and management of pulmonary nodules (PNs) to optimize early detection of thoracic cancer and minimize unnecessary harms to patients. The candidate is currently a Post-Doctoral Research Fellow and Attending Pulmonologist at the University of Pennsylvania (Penn). The proposal builds upon Dr. Kim’s previous research training in epidemiology and biostatistics and clinical experience in thoracic oncology. PNs are commonly detected by computed tomography (CT). Lung biopsy, a highly invasive procedure, is required for a definitive diagnosis but carries significant risks and costs. Thus, clinicians face the diagnostic challenge of PN malignancy risk estimation when deciding which patients should undergo a biopsy, and which should be surveilled with repeat imaging. The overall goal of this project is to address the current inadequacy of estimating malignancy risk within the diagnostic process of PN evaluation by assessing the clinical utility and effectiveness of a radiomics-based computer-aided diagnosis (CAD) tool. This novel technology synthesizes quantitative features from raw CT imaging data invisible to the human eye and has been previously demonstrated by the candidate’s team to improve clinicians’ PN diagnostic accuracy. This project’s goal will be accomplished via three complementary specific aims. In Aim 1, a retrospective cohort study will be performed to determine the clinical utility of a CAD-based risk stratification strategy using net reclassification indices, decision curve analysis, and relative utility curves. In Aim 2, a pilot, single-center pragmatic randomized clinical trial will be conducted to compare the clinical effectiveness of a CAD-based risk stratification strategy to usual care for appropriate management of PNs, defined as biopsy or empiric treatment for malignant PNs and surveillance for benign PNs. Finally, in Aim 3, the cost-effectiveness of a CAD-based risk stratification strategy will be evaluated using decision analytic models for a simulated cohort of individuals with newly detected PNs. Dr. Kim has outlined a rigorous training plan of coursework, skills acquisition (with a focus on clinical utility analysis, clinical trial design, decision analytic modeling, and cost-effectiveness analysis), and professional career development. To realize this vision, he has assembled a distinguished, multidisciplinary mentorship and advisory team, led by his primary mentor, Dr. Anil Vachani, the Director of Clinical Research in the Section of Interventional Pulmonology and Thoracic Oncology at Penn, and co-mentor, Dr. Katharine Rendle, Deputy Director for Research at the Penn Center for Cancer Care Innovation. Penn provides an outstanding intellectual, collaborative, and supportive environment for this proposal, positioning Dr. Kim to successfully complete the aims and training plan so that he will be a competitive applicant for an R01 award to perform a multicenter comparative effectiveness trial assessing PN malignancy risk stratification strategies.

项目概要/摘要该提案描述了一个全面的 5 年指导职业发展计划，其目标是培训成为专注于改善诊断的领先独立医师科学家的候选人评估和管理肺结节 (PN)，以优化胸癌的早期检测和尽量减少对患者不必要的伤害。该候选人目前是一名博士后研究员，并且宾夕法尼亚大学 (Penn) 的主治肺科医师该提案以 Kim 博士之前的提案为基础。流行病学和生物统计学方面的研究培训以及胸部肿瘤学的临床经验。通常需要通过计算机断层扫描 (CT) 进行检测，肺活检是一种高度侵入性的手术。明确的诊断，但会带来巨大的风险和成本，因此，退伍军人面临着 PN 的诊断挑战。在决定哪些患者应该接受活检以及哪些患者应该接受活检时进行恶性肿瘤风险评估该项目的总体目标是解决当前的不足之处。通过评估 PN 评估的临床效用和评估诊断过程中的恶性肿瘤风险基于放射组学的计算机辅助诊断 (CAD) 工具的有效性综合了这项新技术。来自人眼不可见的原始 CT 成像数据的特征，并且之前已被定量由候选人团队提高 PN 诊断准确性。通过三个互补的具体目标来实现在目标 1 中，将进行一项回顾性队列研究。使用净重分类指数确定基于 CAD 的风险分层策略的临床效用，目标 2 是一项试点、单中心实用随机临床试验。将进行试验以比较基于 CAD 的风险分层策略与通常策略的临床有效性对 PN 进行适当管理的护理，定义为对恶性 PN 进行活检或经验性治疗，以及最后，在目标 3 中，基于 CAD 的风险分层策略的成本效益。将使用决策分析模型对具有新检测到的 PN 的模拟个体队列进行评估。 Kim 博士概述了严格的课程作业、技能获取培训计划（重点关注临床实用性）分析、临床试验设计、决策分析建模和成本效益分析）和专业为了实现这一愿景，他组建了杰出的多学科导师和团队。顾问团队由他的主要导师、临床研究部主任 Anil Vachani 博士领导。宾夕法尼亚大学介入肺科和胸部肿瘤科，联合导师凯瑟琳·伦德尔 (Katharine Rendle) 博士，副教授宾夕法尼亚大学癌症护理创新中心的研究主任提供了出色的服务。该提案的智力、协作和支持环境使金博士能够成功完成目标和培训计划，以便他成为 R01 奖项的竞争性申请人，以执行评估 PN 恶性肿瘤风险分层策略的多中心比较有效性试验。